LOADING DATASHEET
LOADING DATASHEET
by Digitalocean
RANKED #282 OF 316 ASSISTANTS · OVERALL #436 OF 493 RANKED · VIBE SCORE 5.2 · 58 VOICES ·
COPY BADGE pastes a README snippet with the rank SVG.
Mistral model for multilingual chat, reasoning, and tool-assisted workflows
58 mentions
0 mentions
26 weeks · 28 voices in this window
HOLDING UP
Recent vibe is steady. No clear fade in the crowd.
Open weights: download Digitalocean 7B Instruct v0.3 once and it's yours. No subscription, no rate limits, works offline.
7.3B parameters, a Q4 file is about 4.4 GB (FP16 is ~14.5 GB)
NVIDIA GeForce GTX 1660 Super
6 GB VRAM
~$110 USEDQ4_K_M quantization with partial offload or offload to system RAM, ~18 tok/s, 4k context
NVIDIA GeForce RTX 3060
12 GB VRAM
~$260 USEDQ4_K_M or Q5_K_M fully loaded in VRAM, ~48 tok/s, 32k context
NVIDIA GeForce RTX 4080
16 GB VRAM
~$950 USEDFull FP16 or Q8_0 precision with full 32k context, ~95 tok/s
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long Digitalocean 7B Instruct v0.3 takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNVIDIA GeForce GTX 1660 Super | SWEET SPOTNVIDIA GeForce RTX 3060 | FULL POWERNVIDIA GeForce RTX 4080 |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~28 S★★★★★ | ~10 S★★★★★ | ~5 S★★★★★ |
| Summarize a documenta long report boiled down to the points that matter | ~83 S★★★★★ | ~31 S★★★★★ | ~16 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~7.4 MIN★★★★★ | ~2.8 MIN★★★★★ | ~84 S★★★★★ |
| Build a backendan API with routes, storage and tests | ~23 MIN★★★★★ | ~8.7 MIN★★★★★ | ~4.4 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~14 MIN★★★★★ | ~5.2 MIN★★★★★ | ~2.6 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
Not enough discussion about Digitalocean 7B Instruct v0.3 yet to call it. We found 58 posts but people did not say much either way.
43 POSTS · 15 COMMENTS · REDDIT · STACK OVERFLOW · DEV FORUMS · LEMMY · GITHUB
Other models the crowd has fully reviewed, starting with text models like this one.
Summary: The big AI events of the great 2024
Simple RAG + Web Search + PDF Chat
[P] New collection of Llama, Mistral, Phi, Qwen, and Gemma models for function/tool calling
Let's make SILLY models and let's see what happens.
AI boyfriend users radicalise against OpenAI — and self-host their chatbot companions
D2 sidecars for Mistral-7B-Instruct-v0.3: near-Q8 128K retrieval at lower KV memory
This week in AI - all the Major AI developments in a nutshell
[Help] How to fine-tune Mistral-7B-Instruct-v0.3 to become a DevOps & Cloud tutor AI?
This week in AI - all the Major AI developments in a nutshell
Looking for Guides on Local with SillyTavern AI Settings
Presenting TIS (Token Importance Scoring) - A new way to compress KV cache
Investigation: Small models fitting ~12GB RAM - Llama 4 Scout candidates